Membrane and Water Treatment

  • 제1권3호
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  • Pages.181-192
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  • 2010
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  • 2005-8624(pISSN)
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  • 2092-7037(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of retention of uncharged solutes in nanofiltration by means of molecular descriptors

  • Nowaczyk, Alicja (Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University) ;
  • Nowaczyk, Jacek (Faculty of Chemistry, Nicolaus Copernicus University) ;
  • Koter, Stanislaw (Faculty of Chemistry, Nicolaus Copernicus University)
  • 투고 : 2009.10.01
  • 심사 : 2010.03.22
  • 발행 : 2010.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

A linear quantitative structure-property relationship (QSPR) model is presented for the prediction of rejection in permeation through membrane. The model was produced by using the multiple linear regression (MLR) technique on the database consisting of retention data of 25 pesticides in 4 different membrane separation experiments. Among the 3224 different physicochemical, topological and structural descriptors that were considered as inputs to the model only 50 were selected using several criteria of elimination. The physical meaning of chosen descriptor is discussed in detail. The accuracy of the proposed MLR models is illustrated using the following evaluation techniques: leave-one-out cross validation procedure, leave-many-out cross validation procedure and Y-randomization.

키워드

참고문헌

  1. Afantitis, A., Melagraki, G., Sarimveis, H., Koutentis, P.A., Markopoulos, J. and Igglessi-Markopoulou, O. (2006), "A novel QSAR model for modeling and predicting induction of apoptosis by 4-Aryl-4H-chromenes", Bioorgan. Med. Chem., 14(19), 6686-6694. https://doi.org/10.1016/j.bmc.2006.05.061
  2. Allison, P.D. (1999), Multiple Regression: A Primer, Pine Forge Press.
  3. Baumann, K. (2005), "Chance correlation in variable subset regression: influence of the objective function, the selection mechanism, and ensemble averaging", QSAR Comb. Sci., 24(9), 1033-1046. https://doi.org/10.1002/qsar.200530134
  4. Belsley, D.A., Kuh, E. and Welsch, R.E. (2005), Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley-Interscience.
  5. Bland, M. (2000), Introduction to Medical Statistics, 3rd Edition, Oxford University Press, London.
  6. Broto, P., Moreau, G. and Vandycke, C. (1984), "Molecular structures: perception, autocorrelation descriptor and QSAR studies. System of atomic contributions for the calculation of the n-octanol/water coefficients", Eur. J. Med. Chem., 19(1), 71-78.
  7. Broto, P. and Devillers, J. (1990), "Autocorrelation of properties distributed on molecular graphs", Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology, Eds. Karcher,W. and Devillers, J., Kluwer, Dordrecht.
  8. Carhart, R.E., Smith, D.H. and Venkataraghavan, R. (1985), "Atom pairs as molecular features in structureac tivity studies: definition and applications", J. Chem. Comput. Sci., 25(2), 64-73. https://doi.org/10.1021/ci00046a002
  9. Consonni, V., Todeschini, R. and Pavan, M. (2002), "Structure/Response Correlations And Similarity/Diversity Analysis By Getaway Descriptors. 1. Theory Of The Novel 3d Molecular Descriptors", J. Chem. Comput. Sci., 42(3), 682-692. https://doi.org/10.1021/ci015504a
  10. Consonni, V. and Todeschini, R. (2002), Rational Approaches to Drug Design, Vol. 42, Prous Science, Barcelona.
  11. Duchowicz, P.R., Talevi, A., Bruno-Blanch, L.E. and Castro, E.A. (2008), "New QSPR study for the prediction of aqueous solubility of drug-like compounds", Bioorgan. Med. Chem., 16(17), 7944-7955. https://doi.org/10.1016/j.bmc.2008.07.067
  12. Estrada, E., Guevara, N. and Gutman, I. (1998), "Extension of edge connectivity index. relationships to line graph indices and QSPR applications", J. Chem. Comput. Sci., 38(3), 428-431. https://doi.org/10.1021/ci970091s
  13. Frisch, M.J., Trucks, G.W., Schlegel, H.B., et al. (2004), Gaussian 03, Revision D.01, Gaussian, Inc.
  14. Ghose, A.K. and Crippen, G.M. (1986), "Atomic Physicochemical Parameters for Three-Dimensional Structure- Directed Quantitative Structure-Activity Relationships I. Partition coefficients as a Measure of hydrophobicity", J. Comput. Chem., 7(4), 565-577. https://doi.org/10.1002/jcc.540070419
  15. Golbraikh, A. and Tropsha, A. (2002), "Beware of $q^{2}$!", J. Mol. Graph. Model., 20(4), 269-276. https://doi.org/10.1016/S1093-3263(01)00123-1
  16. Gramatica, P. (2007), "Principles of QSAR models validation: internal and external", QSAR Comb. Sci., 26(5), 694. https://doi.org/10.1002/qsar.200610151
  17. Hemmer, M.C., Steinhauer, V. and Gasteiger, J. (1999), "The Prediction of the 3D Structure of Organic Molecules from Their Infrared Spectra", Vib. Spectrosc., 19(1), 151-164. https://doi.org/10.1016/S0924-2031(99)00014-4
  18. Hu, R., Yin, C., Wang, Y., Lu, C. and Ge, T. (2008), "QSPR study on GC relative retention time of organic pesticides on different chromatographic columns", J. Sep. Sci., 31(3), 2434-2443. https://doi.org/10.1002/jssc.200800026
  19. Jantschi, L., Bolboac , S.D. and Diudea, M.V. (2007), "Chromatographic retention times of polychlorinated biphenyls: from structural information to property characterization", Int. J. Mol. Sci., 8(11), 1125-1157.
  20. Katritzky, A.R., Mu, L., Lobanov, V.S. and Karelson, M. (1996), "Correlation of boiling points with molecular structure. 1. A training set of 298 diverse organics and a test set of 9 simple inorganics", J. Phys. Chem., 100(24), 10400-10407. https://doi.org/10.1021/jp953224q
  21. Kier, L.B. and Hall, L.H. (1981), "Derivation and significance of valence molecular connectivity", J. Pharm. Sci., 70, 583-589. https://doi.org/10.1002/jps.2600700602
  22. Kier, L.B. and Hall, L.H. (1986), Molecular Connectivity in Structure-Activity Analysis, RSP-Wiley, Chichetser.
  23. Kim, K.J., Fane, A.G., Ben Aim, R., Liu, M.G., Jonsson, G., Tessaro, I.C., Broek, A.P. and Bargeman, D. (1994), "A comparative study of techniques used for porous membrane characterization: pore characterization", J. Membrane Sci., 87(1-2), 35-46. https://doi.org/10.1016/0376-7388(93)E0044-E
  24. Kiso, Y., Nishimura, Y., Kitao, T. and Nishimura, K. (2000), "Rejection properties of non-phenilic pesticides with nanofiltration membranes", J. Membrane Sci., 171(2), 229-237. https://doi.org/10.1016/S0376-7388(00)00305-7
  25. Kiso, Y., Sugiura, Y., Kitao, T. and Nishimura, K. (2001), "Effects of hydrophobicity and molecular size on rejection of aromatic pesticides with nanofiltration membranes", J. Membrane Sci., 192(1-2), 1-10. https://doi.org/10.1016/S0376-7388(01)00411-2
  26. Koltuniewicz, A.B. and Drioli, E. (2008), Membranes in Clean Technologies: Theory and Practice, Vol.1, 2, WILEY-VCH.
  27. Koros, W.J., Ma, Y.H. and Shimidzu, T. (1996), "Terminology for membranes and membrane processes", J. Membrane Sci., 120(2),149-159. https://doi.org/10.1016/0376-7388(96)82861-4
  28. Koter S., Gilewicz- ukasik, B., Nowaczyk, A., Nowaczyk, J. (2009), "Application of molecular descriptors to the prediction of retention in organic solvent nanofiltration", Pol. J. Chem., 83(12), 2137-2152.
  29. Li, L., Xie, S., Cai, H., Bai, X. and Xue, Z. (2008), "Quantitative structure-property relationships for octanolwater partition coefficients of polybrominated diphenyl ethers", Chemosphere, 72(10), 1602-1606. https://doi.org/10.1016/j.chemosphere.2008.04.020
  30. Libotean, D., Giralt, J., Rallo, R., Cohen, Y., Giralt, F., Ridgway, H.F., Rodriguez, G. and Phipps, D. (2008), "Organic compounds passage through RO membranes", J. Membrane Sci., 313(1-2), 23-43. https://doi.org/10.1016/j.memsci.2007.11.052
  31. Schuur, J.H. and Gasteiger, J. (1996), Software Development in Chemistry, Vol. 10, Fachgruppe Chemie- Information-Computer (CIC), Frankfurt am Main.
  32. Schuur, J.H., Selzer, P. and Gasteiger, J. (1996), "The coding of the three-dimensional structure of molecules by molecular transforms and its application to structure - spectra correlations and studies of biological activity", J. Am. Chem. Soc., 36(2), 334-344.
  33. Scott, K. (1995), Handbook of Industrial Membranes, 1st Edition, Elsevier Science.
  34. Tanford, C. (1961), Physical Chemistry of Macromolecules, Wiley, New York.
  35. Todeschini, R. and Consonni, V. (2000), Handbook of Molecular Descriptors, Wiley-VCH New York.
  36. Toghiani, R.K., Toghiani, H., Maloney, S.W. and Boddu, V.M. (2008), "Prediction of physicochemical properties of energetic materials", Fluid Phase Equilibr., 264(1-2), 86-92. https://doi.org/10.1016/j.fluid.2007.10.018
  37. Topliss, J.G. and Costello, R.J. (1972), "Chance correlations in structure-activity studies using multiple regression analysis", J. Med. Chem., 15(10), 1066 -1068. https://doi.org/10.1021/jm00280a017
  38. Toropov, A.A., Toropova, A.P. and Raska, I. (2008), "QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES", Eur. J. Med. Chem., 43(4), 714-740. https://doi.org/10.1016/j.ejmech.2007.05.007
  39. Tragardh, G. and Olund, K. (1986), "A method for characterization of ultrafiltration membranes", Desalination, 58(3), 187-198. https://doi.org/10.1016/0011-9164(86)87003-5
  40. Tropsha, A., Gramatica, P. and Gombar, V.K. (2003), "The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models", QSAR Comb. Sci., 22(1), 69-77. https://doi.org/10.1002/qsar.200390007
  41. Tsapiuk, E.A. (1996), "Ultrafiltration separation of aqueous solutions of poly(ethylene glycol)s on the dynamic membrane formed by gelatin", J. Membrane Sci., 116(1), 17-29. https://doi.org/10.1016/0376-7388(95)00320-7
  42. Yangali-Quintanilla, V., Verliefde, A., Kim, T.-U., Sadmani, A., Kennedy, M. and Amy, G. (2009), "Artificial neural network models based on QSAR for predicting rejection of neutral organic compounds by polyamide nanofiltration and reverse osmosis membranes", J. Membrane Sci., 342(1-2), 251-262. https://doi.org/10.1016/j.memsci.2009.06.048